AI for Science

Agentic laboratories and automated research systems, from scientific world models to benchmarks for drug discovery.

I am particularly interested in AI for Science, and work with universities and research institutes to design and build agentic laboratories and automated research systems. With a broad group of collaborators across the sciences, I co-authored a perspective on agentic laboratories of the future and world models for scientific discovery (Fu et al., 2026). Epi (Huang et al., 2026) is a persistent, self-improving research agent that carries investigations across model sessions with hierarchical memory, branching, and independent review; it has produced 120 record-setting results, from sharper rigorous bounds on the n-queens constant to a computer-assisted proof of a case of Tuza’s conjecture. DrugDiscoveryBench (Akyürek et al., 2026), built with Phylo, measures how coding agents handle early-stage drug-discovery tasks. Earlier at Microsoft Research, I co-developed FaST-LMM (Lippert et al., 2011), a linear mixed model for genome-wide association studies.

Publications

2026

arXiv

Epi: Towards a Persistent, Self-Improving Agent for Autonomous Research

Yue Huang, Zhangchen Xu, Xiaonan Luo, Zheyuan Liu, Lichi Li, Yiping Wang, Peiyang Song, Eric Gabriel Ramos, Enze Xie, Olexandr Isayev, Paul Liang, Nouha Dziri, Huan Sun, Jianfeng Gao, Ying Liu, and 4 more authors

arXiv preprint arXiv:2610.24944, Oct 2026

arXiv Bib

@article{huang2026epi,
  title = {Epi: Towards a Persistent, Self-Improving Agent for Autonomous Research},
  author = {Huang, Yue and Xu, Zhangchen and Luo, Xiaonan and Liu, Zheyuan and Li, Lichi and Wang, Yiping and Song, Peiyang and Ramos, Eric Gabriel and Xie, Enze and Isayev, Olexandr and Liang, Paul and Dziri, Nouha and Sun, Huan and Gao, Jianfeng and Liu, Ying and Sra, Misha and Zhang, Xiangliang and Pentland, Alex and Chen, Zichen},
  journal = {arXiv preprint arXiv:2610.24944},
  year = {2026},
  month = oct,
}

Preprint

Agentic Laboratories of the Future: Towards World Models for Scientific Discovery

Kelsey Fu, Luna Lyu, Shuzhen Li, Suozhi Huang, Suheng Xu, Joy Zheng, Xuefeng Liu, Shilong Liu, Greg Barbone, Ying Liu, Linxi Jim Fan, Yuke Zhu, Matthew Davis, Kaiyi Jiang, Sherry Yang, and 40 more authors

Preprints.org, Aug 2026

Bib HTML PDF

@article{fu2026agentic,
  title = {Agentic Laboratories of the Future: Towards World Models for Scientific Discovery},
  author = {Fu, Kelsey and Lyu, Luna and Li, Shuzhen and Huang, Suozhi and Xu, Suheng and Zheng, Joy and Liu, Xuefeng and Liu, Shilong and Barbone, Greg and Liu, Ying and Fan, Linxi Jim and Zhu, Yuke and Davis, Matthew and Jiang, Kaiyi and Yang, Sherry and Kuang, Xu and Luo, Yuan and Dominici, Francesca and Curtis, Christina and Qiu, Xiaojie and Abadie, Alberto and Uhler, Caroline and Tang, Mary and Wu, Joseph C. and Li, Ju and Toettcher, Jared and Avalos, Jos{\'e} L. and Rojansky, Rebecca and Zhao, Huimin and Ioannidis, John P. A. and Rand, Barry and Lundberg, Emma and Rosen, Andrew S. and Liu, Zhuang and Kelley, Shana and Xiong, Chenyan and Engelhardt, Barbara E. and Montine, Thomas and Theodoris, Christina V. and Pollard, Katherine S. and Li, Fuxin and Zhang, Xueyue and Peng, Tianyi and Basov, Dmitri and Qian, Xiang and Xing, Eric and Yazdani, Ali and Leskovec, Jure and Shah, Nigam and Bao, Zhenan and Perona, Pietro and Wu, Sanfeng and Brangwynne, Clifford and Cong, Le and Wang, Mengdi},
  journal = {Preprints.org},
  year = {2026},
  month = aug,
  doi = {10.20944/preprints202608.0213.v1},
}

Report

DrugDiscoveryBench: Can Coding Agents Assist Early-Stage Drug Discovery?

Afra Feyza Akyürek, Xinming Tu, Alec Gutmanstein, Jason Qin, Divyansh Agarwal, Sofia Monasdotter, Sergey Chekhov, Brenda Hernandez Villegas, Kirill Chugunov, Judah Engel, Veronica Chatrath, Oscar Kavanagh, Geobio Boo, Ernesto Hernandez, Ying Liu, and 7 more authors

Scale AI technical report, with Phylo , Jun 2026

Bib HTML PDF

@techreport{akyurek2026drugdiscoverybench,
  title = {{DrugDiscoveryBench}: Can Coding Agents Assist Early-Stage Drug Discovery?},
  author = {Aky{\"u}rek, Afra Feyza and Tu, Xinming and Gutmanstein, Alec and Qin, Jason and Agarwal, Divyansh and Monasdotter, Sofia and Chekhov, Sergey and Hernandez Villegas, Brenda and Chugunov, Kirill and Engel, Judah and Chatrath, Veronica and Kavanagh, Oscar and Boo, Geobio and Hernandez, Ernesto and Liu, Ying and Xue, Yuan and Sabharwal, Aakash and Zhang, Daniel Yue and Doctor, Zainab and Qu, Yuanhao and He, Yunzhong and Hassaan, Sami},
  institution = {Scale AI (with Phylo)},
  year = {2026},
  month = jun,
}

2011

Nat. Methods

FaST Linear Mixed Models for Genome-Wide Association Studies

Christoph Lippert, Jennifer Listgarten, Ying Liu, Carl M. Kadie, Robert I. Davidson, and David Heckerman

Nature Methods, 2011

Bib HTML Code

@article{lippert2011fast,
  title = {{FaST} Linear Mixed Models for Genome-Wide Association Studies},
  author = {Lippert, Christoph and Listgarten, Jennifer and Liu, Ying and Kadie, Carl M. and Davidson, Robert I. and Heckerman, David},
  journal = {Nature Methods},
  volume = {8},
  number = {10},
  pages = {833--835},
  year = {2011},
  doi = {10.1038/nmeth.1681},
}